Accurate diagnosis of diseases has always been one of the fundaments in health care. The process of making a diagnosis can take many forms, like symptom-based, patient history-based and test-based diagnosis. In test-based diagnosis, measured levels of biomarkers in body-fluids (e.g. IgE in blood is indicative of allergy, and sugar in the urine is indicative of diabetes) are used for pinpointing the disease of the patient. Furthermore, physicians tend to combine symptom-based and test-based diagnosis in order to accurately state the underlying disease.
In recent years, the availability of tests for molecular biomarkers has increased immensely. It is now common that a panel of tests is performed and the result of these, in combination with patient history, forms the basis for diagnosis. As long as the number of tests is less than around 10, this is acceptable for the average physician to handle “in his head”. However, when the physician has to make decisions based on 20-100 individual test results, the risk for misinterpretation and confusion increases drastically.
In order to improve the decisions made by the physician or decrease the number of misinterpretations and errors made, clinical decision support systems (CDSSs) have been developed, designed to improve clinical decision-making related to diagnostic or therapeutic processes of care. CDSSs address activities in many fields, such as the selection of drugs (see e.g. “A computer-assisted management program for antibiotics and other antiinfective agents” by Evans et al., N Engl J. Med. 1998; 338:232-238) and the screening for latent tuberculosis infection (see e.g. “Using computerized clinical decision support for latent tuberculosis infection screening” by Steele et al., Am J Prey Med. 2005; 28(3):281-4). Furthermore, there are different support tools for interpretation of images (see e.g. “Automated evidence-based critiquing of orders for abdominal radiographs: impact on utilization and appropriateness” by Harpole et al., J Am Med Inform Assoc. 1997; 4:511-521, and US 2005/0102315).
Most CDSSs have approximately the same structure, e.g. as described in “Clinical decision support systems: perspectives in dentistry” by Mendonca in J Dent Educ. 2004 June; 68(6):589-97, which is incorporated by reference herein. In a typical CDSS, there is a working memory (often referred to as a database) containing patient data, a decision (or inference) engine which uses a categorized knowledge base (containing e.g. probabilities for disease given a test result). There may be an explanation module available, which transforms the output from the decision engine into messages with context.
One CDSS for interpretation of test results in a diagnostic situation is disclosed in WO 2005/103300, in which a statistical pattern recognition algorithm is applied to a panel of test results relating to autoimmune diseases. The algorithm compares the panel of test results with a multitude of reference data sets for previously diagnosed patients, each reference data set including values for each of a plurality of specific autoantibodies and a diagnosed disease. The algorithm applies a k-nearest neighbor process to the panel of test results and the reference data sets to produce a statistically derived decision indicating whether the panel of test results is associated with none, or one or more of the specific diseases.
WO 96/12187 discloses an automated diagnostic system capable of complex pattern recognition from multivariate laboratory data, using trained neural networks.
The prior art further comprises US 2006/0013773, which discloses a technique for correlating blood types to food allergies and food hypersensitivities.
U.S. Pat. No. 5,692,220 discloses a decision support system for hematopathology diagnosis, in which test results are input to a decision engine which compares them with patterns corresponding to specific patient conditions. The matched patterns are arranged in a hierarchy in accordance with predetermined rules.
A common problem in designing a prior art CDSS is that the complexity of its decision engine increases rapidly with the number of available test results. Clearly, this causes problems in fields where a large number of biomarker tests are available, and where new tests are constantly being developed. One such field is allergy and autoimmune diseases, in which several hundreds, if not thousands, of different biomarker tests are available.
Furthermore, it may be desirable to combine test results with demographics and observed symptoms, in order to improve the accuracy of the diagnosis provided by the decision engine and to allow the decision engine to suggest relevant follow-up tests. This will increase complexity of the decision engine even further.